image.png

Group Members:¶

* Sumedh Chinchamalatpure * Aditya Balki * Pranav Pandit * Kirtesh Maskey * Ishwari Joshi

Data Preprocessing¶

In [1]:
import pandas as pd
import numpy as np

C:\Users\sumed\AppData\Roaming\Python\Python311\site-packages\pandas\core\arrays\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
  from pandas.core import (
In [2]:
zom = pd.read_csv(r"C:\Users\sumed\OneDrive\Desktop\zomato\zomato.csv")
In [3]:
zom
Out[3]:
url address name online_order book_table rate votes phone location rest_type dish_liked cuisines approx_cost(for two people) reviews_list menu_item listed_in(type) listed_in(city)
0 https://www.zomato.com/bangalore/jalsa-banasha... 942, 21st Main Road, 2nd Stage, Banashankari, ... Jalsa Yes Yes 4.1/5 775 080 42297555\r\n+91 9743772233 Banashankari Casual Dining Pasta, Lunch Buffet, Masala Papad, Paneer Laja... North Indian, Mughlai, Chinese 800 [('Rated 4.0', 'RATED\n A beautiful place to ... [] Buffet Banashankari
1 https://www.zomato.com/bangalore/spice-elephan... 2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ... Spice Elephant Yes No 4.1/5 787 080 41714161 Banashankari Casual Dining Momos, Lunch Buffet, Chocolate Nirvana, Thai G... Chinese, North Indian, Thai 800 [('Rated 4.0', 'RATED\n Had been here for din... [] Buffet Banashankari
2 https://www.zomato.com/SanchurroBangalore?cont... 1112, Next to KIMS Medical College, 17th Cross... San Churro Cafe Yes No 3.8/5 918 +91 9663487993 Banashankari Cafe, Casual Dining Churros, Cannelloni, Minestrone Soup, Hot Choc... Cafe, Mexican, Italian 800 [('Rated 3.0', "RATED\n Ambience is not that ... [] Buffet Banashankari
3 https://www.zomato.com/bangalore/addhuri-udupi... 1st Floor, Annakuteera, 3rd Stage, Banashankar... Addhuri Udupi Bhojana No No 3.7/5 88 +91 9620009302 Banashankari Quick Bites Masala Dosa South Indian, North Indian 300 [('Rated 4.0', "RATED\n Great food and proper... [] Buffet Banashankari
4 https://www.zomato.com/bangalore/grand-village... 10, 3rd Floor, Lakshmi Associates, Gandhi Baza... Grand Village No No 3.8/5 166 +91 8026612447\r\n+91 9901210005 Basavanagudi Casual Dining Panipuri, Gol Gappe North Indian, Rajasthani 600 [('Rated 4.0', 'RATED\n Very good restaurant ... [] Buffet Banashankari
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
51712 https://www.zomato.com/bangalore/best-brews-fo... Four Points by Sheraton Bengaluru, 43/3, White... Best Brews - Four Points by Sheraton Bengaluru... No No 3.6 /5 27 080 40301477 Whitefield Bar NaN Continental 1,500 [('Rated 5.0', "RATED\n Food and service are ... [] Pubs and bars Whitefield
51713 https://www.zomato.com/bangalore/vinod-bar-and... Number 10, Garudachar Palya, Mahadevapura, Whi... Vinod Bar And Restaurant No No NaN 0 +91 8197675843 Whitefield Bar NaN Finger Food 600 [] [] Pubs and bars Whitefield
51714 https://www.zomato.com/bangalore/plunge-sherat... Sheraton Grand Bengaluru Whitefield Hotel & Co... Plunge - Sheraton Grand Bengaluru Whitefield H... No No NaN 0 NaN Whitefield Bar NaN Finger Food 2,000 [] [] Pubs and bars Whitefield
51715 https://www.zomato.com/bangalore/chime-sherato... Sheraton Grand Bengaluru Whitefield Hotel & Co... Chime - Sheraton Grand Bengaluru Whitefield Ho... No Yes 4.3 /5 236 080 49652769 ITPL Main Road, Whitefield Bar Cocktails, Pizza, Buttermilk Finger Food 2,500 [('Rated 4.0', 'RATED\n Nice and friendly pla... [] Pubs and bars Whitefield
51716 https://www.zomato.com/bangalore/the-nest-the-... ITPL Main Road, KIADB Export Promotion Industr... The Nest - The Den Bengaluru No No 3.4 /5 13 +91 8071117272 ITPL Main Road, Whitefield Bar, Casual Dining NaN Finger Food, North Indian, Continental 1,500 [('Rated 5.0', 'RATED\n Great ambience , look... [] Pubs and bars Whitefield

51717 rows × 17 columns

In [4]:
zom.shape
Out[4]:
(51717, 17)
In [5]:
zom.columns
Out[5]:
Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)'],
      dtype='object')
In [6]:
zom.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 51717 entries, 0 to 51716
Data columns (total 17 columns):
 #   Column                       Non-Null Count  Dtype 
---  ------                       --------------  ----- 
 0   url                          51717 non-null  object
 1   address                      51717 non-null  object
 2   name                         51717 non-null  object
 3   online_order                 51717 non-null  object
 4   book_table                   51717 non-null  object
 5   rate                         43942 non-null  object
 6   votes                        51717 non-null  int64 
 7   phone                        50509 non-null  object
 8   location                     51696 non-null  object
 9   rest_type                    51490 non-null  object
 10  dish_liked                   23639 non-null  object
 11  cuisines                     51672 non-null  object
 12  approx_cost(for two people)  51371 non-null  object
 13  reviews_list                 51717 non-null  object
 14  menu_item                    51717 non-null  object
 15  listed_in(type)              51717 non-null  object
 16  listed_in(city)              51717 non-null  object
dtypes: int64(1), object(16)
memory usage: 6.7+ MB

Data Cleaning and Treatment¶

In [7]:
zom.isnull().sum()
Out[7]:
url                                0
address                            0
name                               0
online_order                       0
book_table                         0
rate                            7775
votes                              0
phone                           1208
location                          21
rest_type                        227
dish_liked                     28078
cuisines                          45
approx_cost(for two people)      346
reviews_list                       0
menu_item                          0
listed_in(type)                    0
listed_in(city)                    0
dtype: int64
In [8]:
zom["votes"].mean()
Out[8]:
283.69752692538236
In [9]:
zom['rate'] = zom['rate'].str.extract('(\d+\.\d+)')
In [10]:
zom['rate']
Out[10]:
0        4.1
1        4.1
2        3.8
3        3.7
4        3.8
        ... 
51712    3.6
51713    NaN
51714    NaN
51715    4.3
51716    3.4
Name: rate, Length: 51717, dtype: object
In [11]:
zom['rate'] = zom['rate'].astype(float)
In [12]:
zom['rate']
Out[12]:
0        4.1
1        4.1
2        3.8
3        3.7
4        3.8
        ... 
51712    3.6
51713    NaN
51714    NaN
51715    4.3
51716    3.4
Name: rate, Length: 51717, dtype: float64
In [13]:
zom["rate"].mean()
Out[13]:
3.700448817952718
In [14]:
zom['rate'] = zom['rate'].fillna(3.7)
In [15]:
zom['rate'].isnull().sum()
Out[15]:
0
In [16]:
zom = zom.dropna(subset=['phone'])
In [17]:
zom.shape
Out[17]:
(50509, 17)
In [18]:
zom = zom.dropna(subset=['location'])
In [19]:
zom.shape
Out[19]:
(50509, 17)
In [20]:
zom['phone'].isnull().sum()
Out[20]:
0
In [21]:
zom['location'].isnull().sum()
Out[21]:
0
In [22]:
zom.shape
Out[22]:
(50509, 17)
In [23]:
zom.isnull().sum()
Out[23]:
url                                0
address                            0
name                               0
online_order                       0
book_table                         0
rate                               0
votes                              0
phone                              0
location                           0
rest_type                        202
dish_liked                     27209
cuisines                          16
approx_cost(for two people)       12
reviews_list                       0
menu_item                          0
listed_in(type)                    0
listed_in(city)                    0
dtype: int64
In [24]:
zom['approx_cost(for two people)'] = zom['approx_cost(for two people)'].str.replace(',', '')
In [25]:
zom['approx_cost(for two people)']
Out[25]:
0         800
1         800
2         800
3         300
4         600
         ... 
51711     800
51712    1500
51713     600
51715    2500
51716    1500
Name: approx_cost(for two people), Length: 50509, dtype: object
In [26]:
zom['approx_cost(for two people)'] = zom['approx_cost(for two people)'].astype(float)
In [27]:
zom['approx_cost(for two people)'].mean()
Out[27]:
557.5959958017309
In [28]:
zom['approx_cost(for two people)'] = zom['approx_cost(for two people)'].fillna(557.6)
In [29]:
zom.isnull().sum()
Out[29]:
url                                0
address                            0
name                               0
online_order                       0
book_table                         0
rate                               0
votes                              0
phone                              0
location                           0
rest_type                        202
dish_liked                     27209
cuisines                          16
approx_cost(for two people)        0
reviews_list                       0
menu_item                          0
listed_in(type)                    0
listed_in(city)                    0
dtype: int64
In [30]:
zom = zom.dropna(subset=['rest_type'])
In [31]:
zom.shape
Out[31]:
(50307, 17)
In [32]:
zom = zom.dropna(subset=['cuisines'])
In [33]:
zom.shape
Out[33]:
(50291, 17)
In [34]:
del zom['dish_liked']
In [35]:
zom.columns
Out[35]:
Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'phone', 'location', 'rest_type', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)'],
      dtype='object')
In [36]:
del zom['url']
In [37]:
zom.columns
Out[37]:
Index(['address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'phone', 'location', 'rest_type', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)'],
      dtype='object')
In [38]:
del zom['address']
In [39]:
zom.columns
Out[39]:
Index(['name', 'online_order', 'book_table', 'rate', 'votes', 'phone',
       'location', 'rest_type', 'cuisines', 'approx_cost(for two people)',
       'reviews_list', 'menu_item', 'listed_in(type)', 'listed_in(city)'],
      dtype='object')
In [40]:
zom.isnull().sum()
Out[40]:
name                           0
online_order                   0
book_table                     0
rate                           0
votes                          0
phone                          0
location                       0
rest_type                      0
cuisines                       0
approx_cost(for two people)    0
reviews_list                   0
menu_item                      0
listed_in(type)                0
listed_in(city)                0
dtype: int64
In [41]:
zom.head()
Out[41]:
name online_order book_table rate votes phone location rest_type cuisines approx_cost(for two people) reviews_list menu_item listed_in(type) listed_in(city)
0 Jalsa Yes Yes 4.1 775 080 42297555\r\n+91 9743772233 Banashankari Casual Dining North Indian, Mughlai, Chinese 800.0 [('Rated 4.0', 'RATED\n A beautiful place to ... [] Buffet Banashankari
1 Spice Elephant Yes No 4.1 787 080 41714161 Banashankari Casual Dining Chinese, North Indian, Thai 800.0 [('Rated 4.0', 'RATED\n Had been here for din... [] Buffet Banashankari
2 San Churro Cafe Yes No 3.8 918 +91 9663487993 Banashankari Cafe, Casual Dining Cafe, Mexican, Italian 800.0 [('Rated 3.0', "RATED\n Ambience is not that ... [] Buffet Banashankari
3 Addhuri Udupi Bhojana No No 3.7 88 +91 9620009302 Banashankari Quick Bites South Indian, North Indian 300.0 [('Rated 4.0', "RATED\n Great food and proper... [] Buffet Banashankari
4 Grand Village No No 3.8 166 +91 8026612447\r\n+91 9901210005 Basavanagudi Casual Dining North Indian, Rajasthani 600.0 [('Rated 4.0', 'RATED\n Very good restaurant ... [] Buffet Banashankari

Analysis¶

Summary Statistics:

In [42]:
summary_stats = zom.describe()
print(summary_stats)

               rate         votes  approx_cost(for two people)
count  50291.000000  50291.000000                 50291.000000
mean       3.702203    287.093496                   558.204573
std        0.395443    811.693496                   440.890479
min        1.800000      0.000000                    40.000000
25%        3.500000      7.000000                   300.000000
50%        3.700000     42.000000                   400.000000
75%        3.900000    201.000000                   700.000000
max        4.900000  16832.000000                  6000.000000

Average Rating:

In [43]:
average_rating = zom['rate'].mean()
print("Average Rating:", average_rating)

Average Rating: 3.7022031775069104

Most Voted Restaurant:

In [44]:
most_voted_restaurant = zom.loc[zom['votes'].idxmax()]['name']
print("Most Voted Restaurant:", most_voted_restaurant)

Most Voted Restaurant: Byg Brewski Brewing Company

Number of Restaurants with Online Ordering & Table Booking:

In [45]:
online_order_counts = zom['online_order'].value_counts()
print("Online Order Counts:\n\n", online_order_counts)

table_booking_counts = zom['book_table'].value_counts()
print("Table Booking Counts:\n\n", table_booking_counts)

Online Order Counts:

 online_order
Yes    30028
No     20263
Name: count, dtype: int64
Table Booking Counts:

 book_table
No     43862
Yes     6429
Name: count, dtype: int64

Average Cost for Two People:

In [46]:
average_cost_for_two = zom['approx_cost(for two people)'].mean()
print("Average Cost for Two People:", average_cost_for_two)

Average Cost for Two People: 558.2045733829113

Locations with the Most Restaurants:

In [47]:
top_locations = zom['location'].value_counts().head(5)
print("Top 5 Locations with the Most Restaurants:\n\n", top_locations)

Top 5 Locations with the Most Restaurants:

 location
BTM                      4981
HSR                      2479
Koramangala 5th Block    2446
JP Nagar                 2200
Whitefield               2079
Name: count, dtype: int64

Restaurant Types Distribution:

In [48]:
restaurant_type_distribution = zom['rest_type'].value_counts()
print("Restaurant Type Distribution:\n", restaurant_type_distribution)

Restaurant Type Distribution:
 rest_type
Quick Bites                   18636
Casual Dining                 10234
Cafe                           3618
Delivery                       2514
Dessert Parlor                 2202
                              ...  
Dessert Parlor, Food Court        2
Cafe, Food Court                  2
Quick Bites, Kiosk                1
Sweet Shop, Dessert Parlor        1
Bakery, Food Court                1
Name: count, Length: 91, dtype: int64

Top Restaurent in a Specific Location

In [49]:
import ipywidgets as widgets
from IPython.display import display

areas = zom['location'].unique()

area_dropdown = widgets.Dropdown(
    options=[''] + list(areas),  
    value='',  
    description='Select Area:',
)

display(area_dropdown)

Dropdown(description='Select Area:', options=('', 'Banashankari', 'Basavanagudi', 'Mysore Road', 'Jayanagar', …
In [50]:
def top_restaurants_in_location(data, location, top_n=5):
    data['location'] = data['location'].str.lower().str.strip()
    location = location.lower().strip()
    selected_location = data[data['location'] == location]
    top_restaurants = selected_location.sort_values(by='rate', ascending=False).head(top_n)
    result_df = top_restaurants[['name', 'approx_cost(for two people)', 'rate']]
    return result_df
In [51]:
selected_area = area_dropdown.value

if selected_area:
    top_restaurants_dataframe = top_restaurants_in_location(zom, selected_area)
    
    print(f"Top restaurants in '{selected_area}':\n")
    print(top_restaurants_dataframe)
else:
    print("Please select an area from the dropdown.")

Please select an area from the dropdown.

Visualization & EDA¶

In [52]:
import seaborn as sns
import matplotlib.pyplot as plt

# Plot of the Restaurant, whether they are accepting online_order or not
sns.countplot(x='online_order', data=zom)
plt.title('Restaurants delivering online or Not')
plt.xlabel('Online Order Availability')
plt.ylabel('Count')
plt.show()
  • The graph titled "Restaurants delivering online or Not" shows that a majority of restaurants, around 30,000, offer online ordering (labeled '1'), while approximately 20,000 do not (labeled '0').
In [53]:
import matplotlib.pyplot as plt

# Top 10 Restaurant Types
plt.figure(figsize=(8, 6))
zom['rest_type'].value_counts().nlargest(10).plot(kind='bar', color='skyblue')
plt.title('Top 10 Restaurant Types')
plt.xlabel('Restaurant Type')
plt.ylabel('Count')
plt.xticks(rotation=45)
plt.show()
In [54]:
import pandas as pd
import matplotlib.pyplot as plt

# Crosstab of rating and listed_in(city)
rel_type = pd.crosstab(zom['rate'], zom['listed_in(city)'])

# Stacked bar plot
rel_type.plot(kind='bar', stacked=True, figsize=(20, 16), colormap='viridis')
plt.title('City - Rating', fontsize=18)
plt.ylabel('City', fontsize=12)
plt.xlabel('Rating', fontsize=12)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()
In [55]:
import seaborn as sns
import matplotlib.pyplot as plt

# Count plot of listed_in(type)
plt.figure(figsize=(10, 10))
sns.countplot(x='listed_in(type)', data=zom)
plt.title('Restaurants in Listed Types')
plt.xlabel('Listed Type')
plt.ylabel('Count')
plt.show()

Heatmap overlay on a map of Bangalore indicating the concentration of restaurants in a particular area.

In [56]:
import pandas as pd

# Getting unique locations
locations = pd.DataFrame({"Name": zom['location'].unique()})
locations.head()
Out[56]:
Name
0 Banashankari
1 Basavanagudi
2 Mysore Road
3 Jayanagar
4 Kumaraswamy Layout
In [57]:
from geopy.geocoders import Nominatim
import pandas as pd
import numpy as np

locations['Name'] = locations['Name'].apply(lambda x: "Bangaluru " + str(x))  # Add 'Bangaluru' to each location name

# Getting latitude and longitude using GeoPy
lat_lon = []
geolocator = Nominatim(user_agent="app")

for location in locations['Name']:
    location_data = geolocator.geocode(location)
    if location_data is None:
        lat_lon.append(np.nan)
    else:
        geo = (location_data.latitude, location_data.longitude)
        lat_lon.append(geo)

locations['geo_loc'] = lat_lon
locations.to_csv('locations.csv', index=False)
In [58]:
locations["Name"] = locations['Name'].apply(lambda x: x.replace("Bangaluru", "").strip())
locations.head()
Out[58]:
Name geo_loc
0 Banashankari (12.8683575, 77.51705850680827)
1 Basavanagudi (12.9405198, 77.5794387)
2 Mysore Road (12.3668428, 76.6620074)
3 Jayanagar (12.9282849, 77.5868566)
4 Kumaraswamy Layout (12.9081487, 77.5553179)
In [59]:
import folium

# Defining a base map function
def generateBaseMap(default_location=[12.97, 77.59], default_zoom_start=12):
    base_map = folium.Map(location=default_location, control_scale=True, zoom_start=default_zoom_start)
    return base_map
In [60]:
# Getting the restaurant count with their name and locations
Rest_locations = pd.DataFrame(zom['location'].value_counts().reset_index())
Rest_locations.columns = ['Name', 'count']
Rest_locations = Rest_locations.merge(locations, on='Name', how="left").dropna()
In [61]:
Rest_locations
Out[61]:
Name count geo_loc
0 BTM 4981 (12.911275849999999, 77.60456543431182)
1 HSR 2479 (12.9136474, 77.6377482)
2 Koramangala 5th Block 2446 (12.9348429, 77.6189768)
3 JP Nagar 2200 (12.9120761, 77.57939318031076)
4 Whitefield 2079 (13.01072835, 77.76149827911348)
... ... ... ...
88 West Bangalore 6 (13.022234699999998, 77.56718324768107)
89 Yelahanka 5 (13.133592700000001, 77.59708590318306)
90 Jakkur 3 (13.0784743, 77.6068938)
91 Rajarajeshwari Nagar 2 (12.9363054, 77.5180883)
92 Peenya 1 (13.0324174, 77.5338863)

89 rows × 3 columns

In [62]:
import folium
from folium.plugins import HeatMap
import numpy as np

lat, lon = zip(*np.array(Rest_locations['geo_loc']))
Rest_locations['lat'] = lat
Rest_locations['lon'] = lon

basemap = generateBaseMap()
HeatMap(Rest_locations[['lat', 'lon', 'count']].values.tolist(), radius=15).add_to(basemap)
Out[62]:
<folium.plugins.heat_map.HeatMap at 0x15d6b3e2750>
In [63]:
basemap
Out[63]:
Make this Notebook Trusted to load map: File -> Trust Notebook
  • We can observe that restaurants tend to concentrate in central bangalore area.
  • As we move away from central,then there is lowering of cluttering of restaurants.
  • This can be vital information for the restaurant owner and entrepreneurs

Scatter Plot for Cost vs. Rating:

In [64]:
plt.figure(figsize=(20, 6))
sns.scatterplot(x='approx_cost(for two people)', y='rate', data=zom)
plt.title('Scatter Plot of Cost vs. Rating')
plt.xlabel('Cost for Two People')
plt.ylabel('Rating')
plt.show()
  • From the scatter plot, we can observe that most of the data points are clustered between 0 and 2000 on the x-axis and between ratings of 3.5 and 5.0 on the y-axis. This suggests that most places have a cost of 2000 or less and a rating between 3.5 and 5.0. As the cost increases, there are fewer data points, indicating that there are fewer high-cost options or they have varied ratings.
  • Tree Map for Location-wise Cost for two people
In [65]:
import plotly.graph_objects as go

zom['approx_cost(for two people)'] = zom['approx_cost(for two people)'].replace('[^\d.]', '', regex=True).astype(float)

avg_cost_data = zom.groupby('location')['approx_cost(for two people)'].mean().reset_index()

fig = go.Figure(go.Treemap(
    labels=avg_cost_data['location'],
    parents=[""] * len(avg_cost_data['location']),
    values=avg_cost_data['approx_cost(for two people)'],
    texttemplate="%{label}<br>Avg Cost: %{value}",
    hoverinfo="label+value+text",
    marker=dict(
        colorscale="Viridis",
        cmin=0,
        cmax=avg_cost_data['approx_cost(for two people)'].max(),
        colorbar=dict(title="Avg Cost for Two People")
    )
))

fig.update_layout(
    title='Location-wise Average Cost for Two People (Tree Map)',
    margin=dict(l=0, r=0, b=0, t=40),
)

fig.show()
In [66]:
from wordcloud import WordCloud
import matplotlib.pyplot as plt

# Assuming your DataFrame has a column 'cuisines'
# Replace 'zom' and column name accordingly if they are different
cuisines_text = ' '.join(zom['cuisines'].dropna())

# Create WordCloud
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(cuisines_text)

# Display the WordCloud image using Matplotlib
plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.title('Most Liked Food by Bangaloreans (Word Cloud)')
plt.show()
In [67]:
import matplotlib.pyplot as plt
import seaborn as sns

restaurant_counts = zom['name'].value_counts()
N = 10
largest_chains = restaurant_counts.head(N)

sns.set(style="whitegrid")

plt.figure(figsize=(12, 8))
colors = sns.color_palette("pastel", N)
bars = largest_chains.sort_values().plot(kind='barh', color=colors)

for bar, count in zip(bars.patches, largest_chains.sort_values()):
    plt.text(bar.get_width() + 0.1, bar.get_y() + bar.get_height()/2 - 0.2,
             str(count), ha='center', va='center', fontsize=10, color='black')

plt.title('Top {} Largest Restaurant Chains in Bangalore'.format(N), fontsize=16)
plt.xlabel('Number of Outlets', fontsize=12)
plt.ylabel('Restaurant Chain', fontsize=12)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
plt.tight_layout()
plt.show()
In [68]:
numeric_columns = zom.select_dtypes(include=['number'])
corr = numeric_columns.corr()
plt.figure(figsize=(15, 8))
sns.heatmap(corr, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)
plt.xticks(rotation=45)
plt.yticks(rotation=0)
plt.show()
In [69]:
import folium
from folium.plugins import HeatMap
import numpy as np

# Assuming Rest_locations and generateBaseMap functions are defined
# Replace these with your actual data and functions

lat, lon = zip(*np.array(Rest_locations['geo_loc']))
Rest_locations['lat'] = lat
Rest_locations['lon'] = lon

basemap = generateBaseMap()
HeatMap(Rest_locations[['lat', 'lon', 'count']].values.tolist(), radius=15).add_to(basemap)

# Save the Folium map as an HTML file
basemap.save("heatmap_map.html")

In a world where data meets dinner, our journey through the Zomato dataset uncovered the spicy details of Bangalore's vibrant food scene. From online ordering dramas to the grand theater of top restaurant categories, we dished out insights garnished with correlations between votes and the cost of a culinary adventure for two. As we bid adieu to this digital banquet, may your future meals be as delightful as our exploration of Bangalore's gastronomic tapestry!¶